The present application is related, generally, to a method and apparatus for detecting obstacles in an outdoor environment. For outdoor environments having a high curvature terrain, reliable identification of an obstacle located therein can be a difficult task. The natural rise and fall of the terrain can easily be misconstrued as an obstacle or even result in an obstacle going undetected.
It is known in the art to use range imagery from stereo vision or radar to detect obstacles in an outdoor environment. Although each approach has certain advantages, each approach also has certain shortcomings and limitations. For example, stereo vision can provide relatively dense range imagery. However, for certain terrain textures and lighting conditions, stereo vision generally can not provide the range resolution necessary to reliably detect small obstacles. Radar is immune to a large range of environmental conditions and works well for detecting relatively large obstacles. However, radar frequently only provides a generalized location for the obstacles that it does detect, and its relatively large beam size can preclude the detection of small obstacles altogether.
It is also known in the art to detect obstacles using laser ranging. Single-axis scanners provide range data from one scan direction in one plane. Although single-axis scanners are relatively inexpensive, the amount of information they provide is limited to information obtained from a single scan direction in one plane. As a result, discontinuities in the range data caused by pitching motion experienced by the single-axis scanner are often misinterpreted as being caused by an obstacle in the spatial area being scanned. In addition, such pitching motion can cause the single-axis scanner to miss an obstacle entirely. Two-axis scanners provide range data from two directions in a cone, and have the range and resolution capability to directly measure the shape of an obstacle. Although two-axis scanners are less susceptible to misinterpreting discontinuities in the range data, they are relatively expensive, difficult to make robust to motion, and require a great amount of processing to handle the data. Thus, it is not generally commercially feasible to use a two-axis scanner for obstacle detection applications.